The role of imaging in the detection and differential diagnosis of neuro-degenerative diseases has increased in recent years. One reason is the emerging availability of quantification techniques that are able to detect subtle changes in the brain which occur in an early phase of the disease, or even in a pre-symptomatic phase. For example, T1-weighted Magnetic Resonance Imaging (MRI) scans are widely used for assessing brain atrophy, which is a key indicator for the onset and progression of many neuro-degenerative diseases. Image analysis techniques help quantify brain atrophy by classifying the brain tissue voxels into different tissue classes such as Gray Matter (GM), White Matter (WM), and Cerobrospinal Fluid (CSF). Brain tissue classification is particularly useful in assessing brain atrophy, since the gray matter volume serves as a biomarker for cortical atrophy.
However, automated tissue classification techniques sometimes provide erroneous tissue classification maps, due to various reasons such as:    i. Remaining bias-field (even after bias-field correction)    ii. Noise    iii. Motion artifacts    iv. Low spatial resolution due to low magnetic field strength    v. LesionsAs a result, typically two types of tissue classification errors may occur in the tissue classification map, namely isolated “blob-like” misclassifications, as well as over- or under-pronunciation of cortical gray matter near its border to white matter.Disadvantageous, such areas of misclassification in a tissue classification map may hinder the further detection and differential diagnosis of neuro-degenerative diseases.
US 2002/0186882 A1 discloses a process and related apparatus for obtaining quantitative data about a 2-dimensional, 3-dimensional image, or other dimensional image, e.g. for classifying and counting the number of entities an image contains. Each entity comprises an entity, structure, or some other type of identifiable portion of the image having definable characteristics. The entities located within an image may have a different shape, color, texture, or other definable characteristic, but still belong to the same classification. In other instances, entities comprising a similar color, and texture may be classified as one type while entities comprising a different color, and texture may be classified as another type. An image may contain multiple entities and each entity may belong to a different class. Thus, the system may quantify image data according to a set of changing criteria and derive one or more classifications for the entities in the image. Once the image data is classified, the total number of entities in the image is calculated and presented to the user. Embodiments provide a way for a computer to determine what kind of entities (e.g., entities) are in an image and counts the total number of entities that can be visually identified in the image. Information utilized during a training process may be stored and applied across different images.